融合多关系的矩阵分解社会化推荐算法

Matrix Factorization Social Recommendation Algorithm Based on Multiple Social Relationships

在现实的社交网络中,用户之间往往存在多种关系,而现有的社会化推荐算法都只考虑一种关系对推荐结果的影响。基于多子网复合复杂网络模型,将用户间的多种社交关系引入用户特征矩阵,提出了基于多关系的矩阵分解社会化推荐算法。通过对2个真实数据集上的实验结果分析,发现加入多种社交关系的矩阵分解社会化推荐方法比传统的矩阵分解算法在推荐准确率方面有显著提高。

In real social networks,there are multiple relationships between users.The existing social recommendation algorithms only consider the impact of one relationship on the recommendation results.Based on the multi-subnet composited complex network model,different social relationships among users are introduced into the user feature matrix.In this paper,matrix factorization social recommendation algorithm based on multi-relationship is proposed.By analyzing the experimental results on two real datasets,the social matrix factorization recommendation method with multi-relationship has a significant improvement in recommendation accuracy compared with the traditional matrix factorization algorithm.